1. Field of the Invention
The present invention generally relates to data mining technologies and, more particularly, to a Data Warehouse Meta model which is a combination of a relational meta model and semantic net.
2. Background Description
Businesses are getting more event-driven and adaptive in nature. They are exposed to large amounts of data every day. For Sense and Respond and Business Process Monitoring (BPM), this data needs to be transformed and stored in a database for analysis purposes. Traditional data warehouse schemas are designed, in general, independent from the business process and source data. For a data warehouse to become adaptive and closely integrated with the operational environment, it has to be sensitive to changes in the business environment. Data schemas of traditional data warehouse solutions are generally not designed to capture sufficient meta data about relationships between the data in the warehouse environment and business process data.
For a business to analyze the data from various perspectives, it is very important that data dimensions have a rich set of attributes that allow defining new relationships between facts and viewing these facts from various perspectives. For example, a dimension geography or product on its own does not provide much information from a business point of view. A business user would like to associate the dimensions with attributes such as geography with region, country, state, city, etc.
In case of the product dimension, a user might want to divide the product further into product category and sub categories, such as the category “shoes”, which could be further sub-divided into sub-categories of “men's shoes” or “ladies' shoes”.
Complex hierarchical relationships are difficult to capture with relational models. For relational models, assumptions have to be made how hierarchical relationships are mapped to database tables. Also, querying hierarchical meta data from a relational database can become very cumbersome and requires detailed knowledge on the underlying schemas for storing the meta data. Also making changes in hierarchical relationships requires complex database operations and updates to the meta model.
On the other hand, semantic nets can describe very well such hierarchical relationships. It is the nature of a semantic net to capture graphs and hierarchies. Semantic nets provide powerful mechanisms to express complex data relationships that can be found in many businesses. Lack of semantically rich queries to link data from a data warehouse with business processes requires significant amounts of programming and integration work today. The next generation data warehouses are getting more “real time” in nature. Hence, the data warehouse will be more integrated with the operational environment and, therefore, it requires more semantic information about the business environment for the integration and to be adaptive to changes.
The deficiencies of current technologies can be summarized as follows:                Lack of meta models to capture information about operational entities, the Business Process Monitoring (BPM) artifacts such as metrics, metric contexts and their relation with data warehouse concepts such as facts and dimension.        Lack of additional meta data that are required to describe the entities of operational source systems and link them to the data of the data warehouse environment.        There is no systematic approach to map and bind entities of the operational source system or BPM artifacts to a data warehouse schema.        Traditional data warehouse solutions require experts to define data schemas which do not allow the capture of rich semantic information.        Current data models and components of data warehouses are difficult to re-use among multiple projects.        Current data warehouses are not adaptive in nature, neither do they support any meta model to make them sensitive to the change. This makes data warehouses very rigid and inflexible with adaptive business processes.        
New data warehouse schemas and components have to be created from scratch for new business problems. It is difficult to reuse meta data about data schemas, components and business data.